diff --git a/timm/scheduler/multistep_lr.py b/timm/scheduler/multistep_lr.py
new file mode 100644
index 00000000..a5d5fe19
--- /dev/null
+++ b/timm/scheduler/multistep_lr.py
@@ -0,0 +1,65 @@
+""" MultiStep LR Scheduler
+
+Basic multi step LR schedule with warmup, noise.
+"""
+import torch
+import bisect
+from timm.scheduler.scheduler import Scheduler
+from typing import List
+
+class MultiStepLRScheduler(Scheduler):
+    """
+    """
+
+    def __init__(self,
+                 optimizer: torch.optim.Optimizer,
+                 decay_t: List[int],
+                 decay_rate: float = 1.,
+                 warmup_t=0,
+                 warmup_lr_init=0,
+                 t_in_epochs=True,
+                 noise_range_t=None,
+                 noise_pct=0.67,
+                 noise_std=1.0,
+                 noise_seed=42,
+                 initialize=True,
+                 ) -> None:
+        super().__init__(
+            optimizer, param_group_field="lr",
+            noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed,
+            initialize=initialize)
+
+        self.decay_t = decay_t
+        self.decay_rate = decay_rate
+        self.warmup_t = warmup_t
+        self.warmup_lr_init = warmup_lr_init
+        self.t_in_epochs = t_in_epochs
+        if self.warmup_t:
+            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
+            super().update_groups(self.warmup_lr_init)
+        else:
+            self.warmup_steps = [1 for _ in self.base_values]
+
+    def get_curr_decay_steps(self, t):
+        # find where in the array t goes,
+        # assumes self.decay_t is sorted
+        return bisect.bisect_right(self.decay_t, t+1)
+
+    def _get_lr(self, t):
+        if t < self.warmup_t:
+            lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
+        else:
+            lrs = [v * (self.decay_rate ** self.get_curr_decay_steps(t)) for v in self.base_values]
+        return lrs
+
+    def get_epoch_values(self, epoch: int):
+        if self.t_in_epochs:
+            return self._get_lr(epoch)
+        else:
+            return None
+
+    def get_update_values(self, num_updates: int):
+        if not self.t_in_epochs:
+            return self._get_lr(num_updates)
+        else:
+            return None
diff --git a/timm/scheduler/scheduler_factory.py b/timm/scheduler/scheduler_factory.py
index 9f7748f4..5f5a4ca8 100644
--- a/timm/scheduler/scheduler_factory.py
+++ b/timm/scheduler/scheduler_factory.py
@@ -5,6 +5,7 @@ from .cosine_lr import CosineLRScheduler
 from .tanh_lr import TanhLRScheduler
 from .step_lr import StepLRScheduler
 from .plateau_lr import PlateauLRScheduler
+from .multistep_lr import MultiStepLRScheduler
 
 
 def create_scheduler(args, optimizer):
@@ -67,6 +68,18 @@ def create_scheduler(args, optimizer):
             noise_std=getattr(args, 'lr_noise_std', 1.),
             noise_seed=getattr(args, 'seed', 42),
         )
+    elif args.sched == 'multistep':
+        lr_scheduler = MultiStepLRScheduler(
+            optimizer,
+            decay_t=args.decay_epochs,
+            decay_rate=args.decay_rate,
+            warmup_lr_init=args.warmup_lr,
+            warmup_t=args.warmup_epochs,
+            noise_range_t=noise_range,
+            noise_pct=getattr(args, 'lr_noise_pct', 0.67),
+            noise_std=getattr(args, 'lr_noise_std', 1.),
+            noise_seed=getattr(args, 'seed', 42),
+        )
     elif args.sched == 'plateau':
         mode = 'min' if 'loss' in getattr(args, 'eval_metric', '') else 'max'
         lr_scheduler = PlateauLRScheduler(